text: # coding: utf-8
  # Copyright (c) Max-Planck-Institut für Eisenforschung GmbH - Computational Materials Design (CM) Department
  # Distributed under the terms of "New BSD License", see the LICENSE file.
  
  import math
  
  import numpy as np
  import os
  from pyiron.base.settings.generic import Settings
  from pyiron.vasp.structure import atoms_from_string, get_species_list_from_potcar
  from pyiron.atomistics.volumetric.generic import VolumetricData
  
  __author__ = "Sudarsan Surendralal"
  __copyright__ = "Copyright 2019, Max-Planck-Institut für Eisenforschung GmbH - " \
                  "Computational Materials Design (CM) Department"
  __version__ = "1.0"
  __maintainer__ = "Sudarsan Surendralal"
  __email__ = "surendralal@mpie.de"
  __status__ = "production"
  __date__ = "Sep 1, 2017"
  
  
  class VaspVolumetricData(VolumetricData):
      """
      General class for parsing and manipulating volumetric static within VASP. The basic idea of the Base class is
      adapted from the pymatgen vasp VolumtricData class
  
      http://pymatgen.org/_modules/pymatgen/io/vasp/outputs.html#VolumetricData
  
      """
  
      def __init__(self):
          super(VaspVolumetricData, self).__init__()
          self.atoms = None
          self._diff_data = None
          self._total_data = None
  
      def from_file(self, filename, normalize=True):
          """
          Parsing the contents of from a file
  
          Args:
              filename (str): Path of file to parse
              normalize (boolean): Flag to normalize by the volume of the cell
          """
          try:
              self.atoms, vol_data_list = self._read_vol_data(filename=filename, normalize=normalize)
          except (ValueError, IndexError, TypeError):
              try:
                  self.atoms, vol_data_list = self._read_vol_data_old(filename=filename, normalize=normalize)
              except (ValueError, IndexError, TypeError):
                  raise ValueError("Unable to parse file: {}".format(filename))
          if self.atoms is not None:
              self._total_data = vol_data_list[0]
              if len(vol_data_list) > 1:
                  self._diff_data = vol_data_list[1]
  
      @staticmethod
      def _read_vol_data_old(filename, normalize=True):
          """
          Convenience method to parse a generic volumetric static file in the vasp like format.
          Used by subclasses for parsing the file. This routine is adapted from the pymatgen vasp VolumetricData
          class with very minor modifications. The new parser is faster
  
          http://pymatgen.org/_modules/pymatgen/io/vasp/outputs.html#VolumetricData.
  
          Args:
              filename (str): Path of file to parse
              normalize (boolean): Flag to normalize by the volume of the cell
  
          """
          if os.stat(filename).st_size == 0:
              s = Settings()
              s.logger.warning("File:" + filename + "seems to be corrupted/empty")
              return None, None
          poscar_read = False
          poscar_string = list()
          dataset = list()
          all_dataset = list()
          dim = None
          dimline = None
          read_dataset = False
          ngrid_pts = 0
          data_count = 0
          atoms = None
          volume = None
          with open(filename, "r") as f:
              for line in f:
                  line = line.strip()
                  if read_dataset:
                      toks = line.split()
                      for tok in toks:
                          if data_count < ngrid_pts:
                              # This complicated procedure is necessary because
                              # vasp outputs x as the fastest index, followed by y
                              # then z.
                              x = data_count % dim[0]
                              y = int(math.floor(data_count / dim[0])) % dim[1]
                              z = int(math.floor(data_count / dim[0] / dim[1]))
                              dataset[x, y, z] = float(tok)
                              data_count += 1
                      if data_count >= ngrid_pts:
                          read_dataset = False
                          data_count = 0
                          all_dataset.append(dataset)
                  elif not poscar_read:
                      if line != "" or len(poscar_string) == 0:
                          poscar_string.append(line)
                      elif line == "":
                          try:
                              atoms = atoms_from_string(poscar_string)
                          except ValueError:
                              pot_str = filename.split("/")
                              pot_str[-1] = "POTCAR"
                              potcar_file = "/".join(pot_str)
                              species = get_species_list_from_potcar(potcar_file)
                              atoms = atoms_from_string(poscar_string, species_list=species)
                          volume = atoms.get_volume()
                          poscar_read = True
                  elif not dim:
                      dim = [int(i) for i in line.split()]
                      ngrid_pts = dim[0] * dim[1] * dim[2]
                      dimline = line
                      read_dataset = True
                      dataset = np.zeros(dim)
                  elif line == dimline:
                      read_dataset = True
                      dataset = np.zeros(dim)
              if not normalize:
                  volume = 1.0
              if len(all_dataset) == 0:
                  s = Settings()
                  s.logger.warning("File:" + filename + "seems to be corrupted/empty")
                  return None, None
              if len(all_dataset) == 2:
                  data = {"total": all_dataset[0] / volume, "diff": all_dataset[1] / volume}
                  return atoms, [data["total"], data["diff"]]
              else:
                  data = {"total": all_dataset[0] / volume}
                  return atoms, [data["total"]]
  
      def _read_vol_data(self, filename, normalize=True):
          """
          Parses the VASP volumetric type files (CHGCAR, LOCPOT, PARCHG etc). Rather than looping over individual values,
          this function utilizes numpy indexing resulting in a parsing efficiency of at least 10%.
  
          Args:
              filename (str): File to be parsed
              normalize (bool): Normalize the data with respect to the volume (Recommended for CHGCAR files)
  
          Returns:
              pyiron.atomistics.structure.atoms.Atoms: The structure of the volumetric snapshot
              list: A list of the volumetric data (length >1 for CHGCAR files with spin)
  
          """ 
          if not os.path.getsize(filename) > 0:
              s = Settings()
              s.logger.warning("File:" + filename + "seems to be empty! ")
              return None, None
          with open(filename, "r") as f:
              struct_lines = list()
              get_grid = False
              n_x = 0
              n_y = 0
              n_z = 0
              n_grid = 0
              n_grid_str = None
              total_data_list = list()
              atoms = None
              for line in f:
                  strip_line = line.strip()
                  if not get_grid:
                      if strip_line == "":
                          get_grid = True
                      struct_lines.append(strip_line)
                  elif n_grid_str is None:
                      n_x, n_y, n_z = [int(val) for val in strip_line.split()]
                      n_grid = n_x * n_y * n_z
                      n_grid_str = " ".join([str(val) for val in [n_x, n_y, n_z]])
                      load_txt = np.genfromtxt(f, max_rows=int(n_grid / 5))
                      load_txt = np.hstack(load_txt)
                      if n_grid % 5 != 0:
                          add_line = np.genfromtxt(f, max_rows=1)
                          load_txt = np.append(load_txt, np.hstack(add_line))
                      total_data = self._fastest_index_reshape(load_txt, [n_x, n_y, n_z])
                      try:
                          atoms = atoms_from_string(struct_lines)
                      except ValueError:
                          pot_str = filename.split("/")
                          pot_str[-1] = "POTCAR"
                          potcar_file = "/".join(pot_str)
                          species = get_species_list_from_potcar(potcar_file)
                          atoms = atoms_from_string(struct_lines, species_list=species)
                      if normalize:
                          total_data /= atoms.get_volume()
                      total_data_list.append(total_data)
                  elif atoms is not None:
                      grid_str = n_grid_str.replace(" ", "")
                      if grid_str == strip_line.replace(" ", ""):
                          load_txt = np.genfromtxt(f, max_rows=int(n_grid / 5))
                          load_txt = np.hstack(load_txt)
                          if n_grid % 5 != 0:
                              add_line = np.genfromtxt(f, max_rows=1)
                              load_txt = np.hstack(np.append(load_txt, np.hstack(add_line)))
                          total_data = self._fastest_index_reshape(load_txt, [n_x, n_y, n_z])
                          if normalize:
                              total_data /= atoms.get_volume()
                          total_data_list.append(total_data)
              if len(total_data_list) == 0:
                  s = Settings()
                  s.logger.warning("File:" + filename + "seems to be corrupted/empty even after parsing!")
                  return None, None
              return atoms, total_data_list
  
      @staticmethod
      def _fastest_index_reshape(raw_data, grid):
          """
          Helper function to parse volumetric data with x-axis as the fastest index into a 3D numpy array
  
          Args:
              raw_data (numpy.ndarray): Raw unprocessed volumetric data which is flattened
              grid (list/turple/numpy.ndarray): Sequence of the integer grid points [Nx, Ny, Nz]
  
          Returns:
              numpy.ndarray: A Nx $\times$ Ny $\times$ Nz numpy array
  
          """
          n_x, n_y, n_z = grid
          total_data = np.zeros((n_x, n_y, n_z))
          all_data = raw_data[0:np.prod(grid)]
          all_indices = np.arange(len(all_data), dtype=int)
          x_indices = all_indices % n_x
          y_indices = all_indices / n_x % n_y
          y_indices = np.array(y_indices, dtype=int)
          z_indices = all_indices / (n_x * n_y)
          z_indices = np.array(z_indices, dtype=int)
          total_data[x_indices, y_indices, z_indices] = all_data
          return total_data
  
      @property
      def total_data(self):
          """
          numpy.ndarray: Total volumtric data (3D)
          """
          return self._total_data
  
      @total_data.setter
      def total_data(self, val):
          self._total_data = val
  
      @property
      def diff_data(self):
          """
          numpy.ndarray: Volumtric difference data (3D)
          """
          return self._diff_data
  
      @diff_data.setter
      def diff_data(self, val):
          self._diff_data = val
  
      def to_hdf(self, hdf5, group_name="volumetric_data"):
          """
          Writes the data as a group to a HDF5 file
  
          Args:
              hdf5 (pyiron.base.generic.hdfio.ProjectHDFio): The HDF file/path to write the data to
              group_name (str): The name of the group under which the data must be stored as
  
          """
          with hdf5.open(group_name) as hdf_vd:
              hdf_vd["TYPE"] = str(type(self))
              hdf_vd["total"] = self.total_data
              if self.diff_data is not None:
                  hdf_vd["diff"] = self.diff_data
  
      def from_hdf(self, hdf5, group_name="volumetric_data"):
          """
          Recreating the VolumetricData instance by reading data from the HDF5 files
  
          Args:
              hdf5 (pyiron.base.generic.hdfio.ProjectHDFio): The HDF file/path to write the data to
              group_name (str): The name of the group under which the data must be stored as
  
          Returns:
              pyiron.atomistics.volumetric.generic.VolumetricData: The VolumetricData instance
  
          """
          with hdf5.open(group_name) as hdf_vd:
              self._total_data = hdf_vd["total"]
              if "diff" in hdf_vd.list_nodes():
                  self._diff_data = hdf_vd["diff"]
  